Job Description
Shape the Future of Intelligence at 2026 Technologies
Are you a visionary engineer ready to define the next era of artificial intelligence? At 2026 Technologies, we are building the autonomous systems that will power the world of tomorrow. We are seeking an exceptional AI Lead Engineer to join our elite R&D division in San Francisco.
In this pivotal role, you will architect scalable machine learning models, lead a team of top-tier data scientists, and push the boundaries of what is possible in Generative AI and Neural Networks. You won't just be writing code; you will be building the infrastructure for tomorrow's reality. If you are passionate about innovation and want to make a tangible impact on the future, we want to hear from you.
Why Join Us?
- Work on cutting-edge technology with a world-class team.
- Competitive compensation and equity packages.
- Flexible remote-first policies with a hub in the heart of SF.
Responsibilities
- Lead Architecture: Design and implement state-of-the-art deep learning architectures for autonomous decision-making systems.
- Team Leadership: Mentor junior engineers and data scientists, conducting code reviews and fostering a culture of technical excellence.
- Model Optimization: Drive the optimization of inference speeds and reduce latency in real-time AI applications.
- Research Integration: Translate the latest academic research in NLP and Computer Vision into production-ready software.
- Collaboration: Partner with product managers and stakeholders to define technical roadmaps and deliver high-impact features.
- Deployment: Manage the end-to-end deployment lifecycle using MLOps tools and CI/CD pipelines.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Machine Learning, or a related field (or equivalent practical experience).
- Experience: 5+ years of professional experience in software engineering with a strong focus on AI/ML.
- Technical Skills: Proficiency in Python, PyTorch, or TensorFlow. Deep understanding of NLP, Transformers, or Reinforcement Learning.
- Infrastructure: Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker/Kubernetes).
- Leadership: Proven track record of leading technical teams and managing large-scale projects.
- Communication: Excellent verbal and written communication skills with the ability to explain complex concepts to diverse audiences.